clinical_trials = ['NOPHO ALL92-2000',
'AAML0531',
'AAML1031',
'Beat AML Consortium',
'TCGA AML',
'CETLAM SMD-09 (MDS-tAML)',
'French GRAALL 2003–2005',
'TARGET ALL',
'AAML03P1',
'Japanese AML05',
'CCG2961']
sample_types = ['Diagnosis', 'Primary Blood Derived Cancer - Bone Marrow',
'Bone Marrow Normal','Primary Blood Derived Cancer - Peripheral Blood',
'Blood Derived Normal','Likely Diagnosis', 'Control (Healthy Donor)',
'Relapse','Recurrent Blood Derived Cancer - Bone Marrow',
'Recurrent Blood Derived Cancer - Peripheral Blood',
'Peripheral Blood Normal']
cols = ['PaCMAP Output','Pathology Class','WHO 2021 Diagnosis','WHO AML 2021 Diagnosis','WHO ALL 2021 Diagnosis','ELN AML 2022 Diagnosis',
'Age (group years)', 'Batch', 'Sex', 'Clinical Trial', 'Sample Type', 'Karyotype', 'Gene Fusion', 'Patient_ID']
# processor = DataProcessor(df_labels.copy(), df_methyl, clinical_trials, sample_types, cols)
# processor.filter_data()
# processor.apply_pacmap()
# processor.join_labels()
# df = processor.df
# # Save output to avoid re-running the code multiple times
# df.to_csv(output_path+'pacmap_output/pacmap_2d_output_acute_leukemia.csv')
df = pd.read_csv(output_path+'pacmap_output/pacmap_2d_output_acute_leukemia.csv', index_col=0)
plotter = BokehPlotter(df, cols, get_custom_color_palette(),
title='The Methylome Atlas of Acute Leukemia',
x_range=(-50, 50), y_range=(-50, 50),
datapoint_size=3)
plotter.plot()